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ds2_AC_markers <- FindAllMarkers(ds2_AC, logfc.threshold = 0.5, min.diff.pct = 0.2, only.pos = F)
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Calculating cluster 3
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ds2_AC_markers_pos <- ds2_AC_markers[ds2_AC_markers$avg_logFC>0, ]
ds2_AC_markers_neg <- ds2_AC_markers[ds2_AC_markers$avg_logFC<0, ]
table(Idents(ds2_AC))
0 1 2 3
1222 828 649 362
dotplot(down_enrich.go, showCategory = 10)
cnetplot(down_enrich.go, showCategory = 10)
emapplot(down_enrich.go, showCategory = 10)
exprmat <- get_data_table(ds2_AC, highvar = T,type = "data")
clusterinfo <- ds2_AC@meta.data[,c("orig.ident","Classification1")]
mbd <- msigdbr(species = "Homo sapiens", category = "C7") # C7 免疫
msigdbr_list <- split(x = mbd$gene_symbol, f = mbd$gs_name)
immo_res <- gsva(exprmat, msigdbr_list, kcdf="Gaussian",method = "gsva", parallel.sz = 6) #gsva 在server上运行
pheatmap(immo_res, show_rownames=1, show_colnames=0,
annotation_col=clusterinfo,fontsize_row=5, wiidth=8, height=6)#绘制热图
es <- data.frame(t(immo_res),stringsAsFactors=F) #添加到单细胞矩阵中,可视化相关通路的在umap上聚集情况,可理解为一个通路即一个基因
dataset1 <- AddclusterinfoData(pbmc, es)
FeaturePlot(dataset1, features = "KEGG_PRIMARY_BILE_ACID_BIOSYNTHESIS", reduction = 'umap')
multi_featureplot()
#GSEA
markers <- FindMarkers(ds2_AC, ident.1 = 3, logfc.threshold = 0.1, min.diff.pct = 0.1)
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markers <- FindMarkers(ds2_AC, ident.1 = 3, logfc.threshold = 0.1, min.diff.pct = 0.1)
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DEGs <- markers$avg_logFC
names(DEGs) = rownames(markers)
DEGs <- sort(DEGs,decreasing = T)
head(DEGs)
SOST SUCNR1 FRZB IGFBP2 DLX6-AS1 LMO2
1.783723 1.570144 1.557937 1.504131 1.495656 1.488827
mdb_c2 <- msigdbr(species = "Homo sapiens", category = "C2")
mdb_kegg <- mdb_c2 [grep("^KEGG", mdb_c2 $gs_name),]
GO_db<-mdb_kegg %>% dplyr::select(gs_name, gene_symbol)
GSEA_res <- clusterProfiler::GSEA(DEGs, TERM2GENE = GO_db,pvalueCutoff = 0.5)
preparing geneSet collections...
GSEA analysis...
Warning in fgsea(pathways = geneSets, stats = geneList, nperm = nPerm, minSize = minGSSize, :
There are ties in the preranked stats (0.09% of the list).
The order of those tied genes will be arbitrary, which may produce unexpected results.
leading edge analysis...
done...
dotplot(GSEA_res,split=".sign")+facet_grid(~.sign)
geneset <- read.table("fibromyo")
dataset1 <- AddModuleScore(dataset1,features = geneset, name = 'fibromyo_score')
f("fibromyo_score1", dataset1, min.cutoff = 0)
dataset1 <- AddModuleScore_UCell(dataset1,features = geneset, name = 'fibromyo_score')
f("V1fibromyo_score", dataset1)
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